TUAI · Towards an Understanding of Artificial Intelligence via a transparent, open and explainable perspective

The focus of the TUAI project will be on the highest quality doctoral training on sustainable the AI that is used in smart manufacturing, smart cities, smart healthcare and smart mobility. This will be achieved by multilateral academic and industry trainings for a new generation of creative, entrepreneurial and innovative researchers Doctoral Candidates (DCs), who will be able to face both the current and future challenges that are associated with the development of Machine Learning (ML) and who will be able to effectively use the latest advances in artificial intelligence and data mining in order to ensure the competitive advantage of the European producers that will use the practical solutions from the TUAI project. The project will focus on ML-based smart services that should first meet the customers needs but with the same importance should focus on protecting the natural environment by creating smart services dedicated for smart mobility, to reduce energy consumption by smart cities and to avoid the losses and suboptimal use of resources in smart manufacturing. The scope of the research will be organised under four research areas (RA): (i) Time Series Analysis, (ii) Sensor Fusion, (iii) Federated Learning and (iv) the sustainability and trustworthiness of the AI solutions. The precise challenges for each RA will be expressed by research questions (RQ), which will be resolved by the individual DC projects. The RQs will not be separate areas of research but will be complementary and intertwined components. Therefore, it is not sufficient to train DCs in one RA and ignore another. The TUAI project will provide holistic research training through secondments and network-wide training activities. The proposed approach will ensure excellent research training for the thirteen DCs who will be prepared for future work both in academic and non-academic sectors and will be able to face the challenges that are associated with the different application areas.